Turbomachinery Generative Design & Optimization

FEA & CFD Based Simulation Design Analysis Virtual prototyping MultiObjective Optimization

Enteknograte’s engineering team optimizes complex geometries with respect to given targets, such as total pressure loss and velocity uniformity with use of advanced simulation tools such as Star-ccm+, Ansys Fluent and Numeca Fine/Turbo. It does so by computing the sensitivities of the geometry itself versus those targets and then modifying it.

The sensitivity information comes directly from the flow field so the optimized shape is the one that fits the given flow best. Unlike traditional design methods that rely on trial and error between a given geometry and flow field predicted by CFD codes, we use 3D inverse design method starts by identifying what we want to do to the fluid flow in terms of 3D pressure field and mathematically derives the optimal geometry to achieve that outcome. This significantly reduces the time taken for each design.

  • Complex geometries sensitivity analysis and optimization, providing designs with improved performance.

  • Innovative design

  • Accurate mathematical approach drives the design to the best shape.

  • 3D Inverse Design method that uses fluid dynamics to directly generate optimum blade shape

  • Including secondary flows, corner separations, tip clearance flow, cavitation, and shock in design of turbomachinery

  • Multi-point/multi-disciplinary optimization of all types of turbomachinery

NASA transonic rotor 37 Aerodynamics CFD Simulation Design Axial Compressor Ansys Fluent Siemens Star-ccm Numeca Fine Turbo MSC Nastran CFX

WE WORK WITH YOU

We pride ourselves on empowering each client to overcome the challenges of their most demanding projects.

Enteknograte offers a Virtual Engineering approach with CFD tools such as MSC Cradle, Ansys Fluent, StarCCM+  for flows simulation and FEA based Codes such as MSC Softwrae(Simufact, Digimat, Nastran, Marc, Actran Acoustic solver), ABAQUS, Ansys and LS-Dyna, encompassing the accurate prediction of in-service loads, the performance evaluation, and the integrity assessment including the influence of manufacturing the components.
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